248,215 research outputs found
A Fisher-Rao metric for paracatadioptric images of lines
No full textIn a central paracatadioptric imaging system a perspective camera takes an image of a scene reflected in a paraboloidal mirror. A 360° field of view is obtained, but
the image is severely distorted. In particular, straight lines in the scene project to circles in the image. These distortions make it diffcult to detect projected lines using standard image processing algorithms. The distortions are removed using a Fisher-Rao metric which is defined on the space of projected lines in the paracatadioptric image. The space of projected lines is divided into subsets such that on each subset the Fisher-Rao metric is closely approximated by the Euclidean metric. Each subset is sampled at the vertices of a square grid and values are assigned to the sampled points using an adaptation of the trace transform. The result is a set of digital images to which standard image processing algorithms can be applied.
The effectiveness of this approach to line detection is illustrated using two algorithms, both of which are based on the Sobel edge operator. The task of line detection is reduced to the task of finding isolated peaks in a Sobel image. An experimental comparison is made between these two algorithms and third algorithm taken from the literature and
based on the Hough transform
Application of the Fisher-Rao metric to ellipse detection
No full textThe parameter space for the ellipses in a two dimensional image is a five dimensional manifold, where each point of the manifold corresponds to an ellipse in the image. The parameter space becomes a Riemannian manifold under a Fisher-Rao metric, which is derived from a Gaussian model for the blurring of ellipses in the image. Two points in the parameter space are close together under the Fisher-Rao metric if the corresponding ellipses are close together in the image. The Fisher-Rao metric is accurately approximated by a simpler metric under the assumption that the blurring is small compared with the sizes of the ellipses under consideration. It is shown that the parameter space for the ellipses in the image has a finite volume under the approximation to the Fisher-Rao metric. As a consequence the parameter space can be replaced, for the purpose of ellipse detection, by a finite set of points sampled from it. An efficient algorithm for sampling the parameter space is described. The algorithm uses the fact that the approximating metric is flat, and therefore locally Euclidean, on each three dimensional family of ellipses with a fixed orientation and a fixed eccentricity. Once the sample points have been obtained, ellipses are detected in a given image by checking each sample point in turn to see if the corresponding ellipse is supported by the nearby image pixel values. The resulting algorithm for ellipse detection is implemented. A multiresolution version of the algorithm is also implemented. The experimental results suggest that ellipses can be reliably detected in a given low resolution image and that the number of false detections
can be reduced using the multiresolution algorithm
Near-Linear Time and Fixed-Parameter Tractable Algorithms for Tensor Decompositions
No full textWe study low rank approximation of tensors, focusing on the Tensor Train and Tucker decompositions, as well as approximations with tree tensor networks and general tensor networks. As suggested by hardness results also shown in this work, obtaining (1+ε)-approximation algorithms for rank k tensor train and Tucker decompositions efficiently may be computationally hard for these problems. Therefore, we propose different algorithms that respectively satisfy some of the objectives above while violating some others within a bound, known as bicriteria algorithms. On the one hand, for rank-k tensor train decomposition for tensors with q modes, we give a (1 + ε)-approximation algorithm with a small bicriteria rank (O(qk/ε) up to logarithmic factors) and O(q ⋅ nnz(A)) running time, up to lower order terms. Here nnz(A) denotes the number of non-zero entries in the input tensor A. We also show how to convert the algorithm of [Huber et al., 2017] into a relative error approximation algorithm, but their algorithm necessarily has a running time of O(qr² ⋅ nnz(A)) + n ⋅ poly(qk/ε) when converted to a (1 + ε)-approximation algorithm with bicriteria rank r. Thus, the running time of our algorithm is better by at least a k² factor. To the best of our knowledge, our work is the first to achieve a near-input-sparsity time relative error approximation algorithm for tensor train decomposition. Our key technique is a method for efficiently obtaining subspace embeddings for a matrix which is the flattening of a Tensor Train of q tensors - the number of rows in the subspace embeddings is polynomial in q, thus avoiding the curse of dimensionality. We extend our algorithm to tree tensor networks and tensor networks on arbitrary graphs. Another way of coping with intractability is by looking at fixed-parameter tractable (FPT) algorithms. We give FPT algorithms for the tensor train, Tucker, and Canonical Polyadic (CP) decompositions, which are simpler than the FPT algorithms of [Song et al., 2019], since our algorithms do not make use of polynomial system solvers. Our technique of using an exponential number of Gaussian subspace embeddings with exactly k rows (and thus exponentially small success probability) may be of independent interest
Inaugural Address by Prime Minister of India P. V. Narasimha Rao
No full textText of the inaugural address by Indian Prime Minister P. V. Narasimha Rao at the CGIAR Mid Term Meeting, May 1994
Empirical likelihood inference for the Rao-Hartley-Cochran sampling design
No full textThe Hartley-Rao-Cochran sampling design is an unequal probability sampling design which can be used to select samples from finite populations. We propose to adjust the empirical likelihood approach for the Hartley-Rao-Cochran sampling design. The approach proposed intrinsically incorporates sampling weights, auxiliary information and allows for large sampling fractions. It can be used to construct confidence intervals. In a simulation study, we show that the coverage may be better for the empirical likelihood confidence interval than for standard confidence intervals based on variance estimates. The approach proposed is simple to implement and less computer intensive than bootstrap. The confidence interval proposed does not rely on re-sampling, linearization, variance estimation, design-effects or joint inclusion probabilities
Tour Report of Dr. P. Vedavyasa Rao, on his participation in the World Conference on Aquaculture followed by visit to certain Fisheries/Aquaculture R&D Institutes in Italy, France and U.K during September 16 to November 3, 1981
No full textTour Report of Dr. P. Vedavyasa Rao, on his participation in the World Conference on Aquaculture followed by visit to certain Fisheries/Aquaculture R&D Institutes in Italy, France and U.K during September 16 to November 3, 198
P. R. Ramachandra Rao, Contemporary Indian Art
No full textFilliozat Jean. P. R. Ramachandra Rao, Contemporary Indian Art. In: Arts asiatiques, tome 25, 1972. p. 207
Economic philosophy of V.K.R.V. Rao
No full textProf. V.K.R.V. Rao falls in the line of great Indian leaders and scholars such as M.G. Ranade, Gandhiji, J.K. Mehta, who emphasised the human values and ethics in the discipline of economics. For him the nature and purpose of economic activity is different from what is prescribed in the conventional economics. By conventional economics we mean the mainstream economics inherited from the West. The present paper attempts to examine the economic philosophy of Rao in the light of his numerous works and the criticism leveled by him against the conventional economics.V.K.R.V. Rao; Indian Economic Thought; Ethics and economics; cooperation; the myth of consumers' sovereignty;Human factor in economic development.
P. R. Ramachandra Rao, Andhra Sculpture
No full textRegnier Rita H. P. R. Ramachandra Rao, Andhra Sculpture . In: Arts asiatiques, tome 42, 1987. pp. 121-122
Reply to " Comments on Capture and Retransmission Control in Mobile Radio"
No full textThe present paper replies to a comment by Nguyen et al. (IEEE Trans. Sel. Areas Commun., vol.24, no.12, p.2340-1, December 2006) on the original paper by Zorzi and Rao (IEEE Trans. Sel. Areas Commun., vol.12, no.8, p.1289-98, October 1994
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